Click here for interactive plots:

https://kforthman.shinyapps.io/COVID19_Interactive_Plots/

Click here to see how cities and counties overlap:

https://kforthman.shinyapps.io/500citiescounties

Compare COVID stats to Neighborhood Factors

data.cor <- cor(county.Demo_and_Covid.allcounties[,-1], use = "complete.obs", method = "spearman")
corrplot.mixed(data.cor, upper = 'ellipse', lower = 'number', tl.pos = 'lt', tl.cex = 1, lower.col = "black", number.cex = 0.5)

Compare COVID stats to 500 cities data and Neighborhood Factors

data.cor2 <- cor(county.Demo_and_Covid.500counties[,-c(1:2)], use = "complete.obs", method = "spearman")
corrplot.mixed(data.cor2, upper = 'ellipse', lower = 'number', tl.pos = 'lt', tl.cex = 1, lower.col = "black", number.cex = 0.5)

corrplot.mixed(data.cor2[7:13,c(1:5, 14:42,6)], upper = 'ellipse', tl.pos = 'lt', tl.cex = 1, lower.col = "black", number.cex = 0.5)

—-Linear Mixed Effects Model —-

this.lme <- lmer("total.cases.percap ~ Affluence + Singletons.in.Tract + Seniors.in.Tract + African.Americans.in.Tract + Noncitizens.in.Tract + High.BP + Binge.Drinking + Cancer + Asthma + Heart.Disease + COPD + Smoking + Diabetes + No.Physical.Activity + Obesity + Poor.Sleeping.Habits + Poor.Mental.Health + Testing_Rate + Hospitalization_Rate + (1 | stateID)", data = county.Demo_and_Covid.500counties)
## Warning: Some predictor variables are on very different scales: consider
## rescaling

## Warning: Some predictor variables are on very different scales: consider
## rescaling
print(summary(this.lme), correlation=TRUE)
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: 
## "total.cases.percap ~ Affluence + Singletons.in.Tract + Seniors.in.Tract + African.Americans.in.Tract + Noncitizens.in.Tract + High.BP + Binge.Drinking + Cancer + Asthma + Heart.Disease + COPD + Smoking + Diabetes + No.Physical.Activity + Obesity + Poor.Sleeping.Habits + Poor.Mental.Health + Testing_Rate + Hospitalization_Rate + (1 | stateID)"
##    Data: county.Demo_and_Covid.500counties
## 
## REML criterion at convergence: -1255.3
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -3.3241 -0.3636 -0.0844  0.1898  6.1906 
## 
## Random effects:
##  Groups   Name        Variance    Std.Dev.
##  stateID  (Intercept) 0.000002398 0.001548
##  Residual             0.000013818 0.003717
## Number of obs: 186, groups:  stateID, 34
## 
## Fixed effects:
##                                  Estimate     Std. Error             df
## (Intercept)                 -0.0154731942   0.0099584384  82.8034083521
## Affluence                    0.0045341127   0.0011252942 120.5433150752
## Singletons.in.Tract          0.0005232057   0.0009261387 159.2482772393
## Seniors.in.Tract             0.0001247819   0.0012020155 164.0158565360
## African.Americans.in.Tract   0.0012085107   0.0010285119 163.2407323254
## Noncitizens.in.Tract         0.0011358376   0.0007739776 130.5342057444
## High.BP                      0.0001371935   0.0001925901 129.3324403743
## Binge.Drinking               0.0002447916   0.0001682112  54.2811644767
## Cancer                      -0.0013227465   0.0011385277 125.1044038400
## Asthma                       0.0009927878   0.0005842391  58.0525802712
## Heart.Disease                0.0022395352   0.0013836000  94.2968173029
## COPD                        -0.0006063137   0.0011398142  90.1085367999
## Smoking                     -0.0000332818   0.0002375475  97.8676487264
## Diabetes                    -0.0006904588   0.0005577040  94.8483913464
## No.Physical.Activity        -0.0000197431   0.0002156239 108.3215669324
## Obesity                      0.0002919261   0.0001816818 131.5096677261
## Poor.Sleeping.Habits        -0.0000507855   0.0001675732 141.2214405664
## Poor.Mental.Health          -0.0000807278   0.0004592101  38.9567616314
## Testing_Rate                 0.0000006361   0.0000002439  43.9976833191
## Hospitalization_Rate        -0.0000657794   0.0000942816  32.4273079601
##                            t value  Pr(>|t|)    
## (Intercept)                 -1.554    0.1241    
## Affluence                    4.029 0.0000983 ***
## Singletons.in.Tract          0.565    0.5729    
## Seniors.in.Tract             0.104    0.9174    
## African.Americans.in.Tract   1.175    0.2417    
## Noncitizens.in.Tract         1.468    0.1446    
## High.BP                      0.712    0.4775    
## Binge.Drinking               1.455    0.1514    
## Cancer                      -1.162    0.2475    
## Asthma                       1.699    0.0946 .  
## Heart.Disease                1.619    0.1089    
## COPD                        -0.532    0.5961    
## Smoking                     -0.140    0.8889    
## Diabetes                    -1.238    0.2188    
## No.Physical.Activity        -0.092    0.9272    
## Obesity                      1.607    0.1105    
## Poor.Sleeping.Habits        -0.303    0.7623    
## Poor.Mental.Health          -0.176    0.8614    
## Testing_Rate                 2.608    0.0124 *  
## Hospitalization_Rate        -0.698    0.4903    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation of fixed effects could have been required in summary()
## 
## Correlation of Fixed Effects:
##             (Intr) Afflnc Sng..T Snr..T A.A..T Nnc..T Hgh.BP Bng.Dr Cancer
## Affluence    0.079                                                        
## Sngltns.n.T  0.009  0.044                                                 
## Snrs.n.Trct  0.522  0.365  0.174                                          
## Afrcn.Am..T  0.146  0.155 -0.407  0.144                                   
## Nnctzns.n.T  0.037  0.101  0.071  0.092 -0.110                            
## High.BP     -0.053  0.211  0.047  0.070 -0.097  0.386                     
## Bing.Drnkng -0.326 -0.167 -0.284 -0.163  0.058 -0.004  0.125              
## Cancer      -0.569 -0.162  0.198 -0.289 -0.072 -0.148 -0.346 -0.093       
## Asthma      -0.382 -0.196 -0.240 -0.203  0.083  0.050  0.164 -0.002  0.049
## Heart.Dises -0.148  0.089 -0.300 -0.146  0.258 -0.123  0.007  0.061 -0.492
## COPD         0.569  0.001  0.155  0.260 -0.036  0.290  0.135  0.077 -0.262
## Smoking     -0.135  0.171 -0.186 -0.093 -0.032  0.043 -0.052 -0.289  0.074
## Diabetes     0.101 -0.333 -0.102 -0.210 -0.302 -0.284 -0.532  0.054  0.244
## N.Physcl.Ac -0.207 -0.011  0.094 -0.016 -0.033 -0.230 -0.069  0.116  0.480
## Obesity      0.010  0.410  0.438  0.305  0.128  0.175 -0.098 -0.230  0.093
## Pr.Slpng.Hb -0.426 -0.403  0.148 -0.335 -0.329 -0.026 -0.177  0.115  0.117
## Pr.Mntl.Hlt -0.354  0.271 -0.070 -0.044  0.090 -0.189 -0.050  0.083  0.326
## Testing_Rat  0.221 -0.046  0.003  0.037  0.028 -0.004 -0.013 -0.027 -0.216
## Hsptlztn_Rt -0.129 -0.197 -0.077 -0.206 -0.044 -0.107 -0.049 -0.096 -0.003
##             Asthma Hrt.Ds COPD   Smokng Diabts N.Ph.A Obesty Pr.S.H Pr.M.H
## Affluence                                                                 
## Sngltns.n.T                                                               
## Snrs.n.Trct                                                               
## Afrcn.Am..T                                                               
## Nnctzns.n.T                                                               
## High.BP                                                                   
## Bing.Drnkng                                                               
## Cancer                                                                    
## Asthma                                                                    
## Heart.Dises  0.275                                                        
## COPD        -0.383 -0.562                                                 
## Smoking      0.089  0.203 -0.495                                          
## Diabetes    -0.114 -0.315 -0.066  0.217                                   
## N.Physcl.Ac  0.028 -0.384 -0.009 -0.330 -0.098                            
## Obesity     -0.270 -0.080  0.153 -0.195 -0.378 -0.045                     
## Pr.Slpng.Hb  0.079  0.235 -0.161 -0.069 -0.025 -0.114 -0.166              
## Pr.Mntl.Hlt -0.252  0.091 -0.468  0.084  0.022  0.050  0.059 -0.166       
## Testing_Rat -0.322 -0.018  0.214  0.136  0.084 -0.315  0.139 -0.145 -0.135
## Hsptlztn_Rt  0.094  0.120 -0.115  0.050  0.006 -0.010 -0.019 -0.003 -0.078
##             Tstn_R
## Affluence         
## Sngltns.n.T       
## Snrs.n.Trct       
## Afrcn.Am..T       
## Nnctzns.n.T       
## High.BP           
## Bing.Drnkng       
## Cancer            
## Asthma            
## Heart.Dises       
## COPD              
## Smoking           
## Diabetes          
## N.Physcl.Ac       
## Obesity           
## Pr.Slpng.Hb       
## Pr.Mntl.Hlt       
## Testing_Rat       
## Hsptlztn_Rt  0.069
## fit warnings:
## Some predictor variables are on very different scales: consider rescaling
this.lme <- lmer("total.cases.percap ~ Affluence + Singletons.in.Tract + Seniors.in.Tract + African.Americans.in.Tract + Noncitizens.in.Tract + High.BP + Binge.Drinking + Cancer + Asthma + Heart.Disease + COPD + Smoking + Diabetes + No.Physical.Activity + Obesity + Poor.Sleeping.Habits + Poor.Mental.Health + (1 | stateID)", data = county.Demo_and_Covid.500counties)

print(summary(this.lme), correlation=TRUE)
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: 
## "total.cases.percap ~ Affluence + Singletons.in.Tract + Seniors.in.Tract + African.Americans.in.Tract + Noncitizens.in.Tract + High.BP + Binge.Drinking + Cancer + Asthma + Heart.Disease + COPD + Smoking + Diabetes + No.Physical.Activity + Obesity + Poor.Sleeping.Habits + Poor.Mental.Health + (1 | stateID)"
##    Data: county.Demo_and_Covid.500counties
## 
## REML criterion at convergence: -2433.1
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -3.8005 -0.3835 -0.0820  0.2778  6.5610 
## 
## Random effects:
##  Groups   Name        Variance    Std.Dev.
##  stateID  (Intercept) 0.000007847 0.002801
##  Residual             0.000013007 0.003607
## Number of obs: 326, groups:  stateID, 51
## 
## Fixed effects:
##                                Estimate   Std. Error           df t value
## (Intercept)                 -0.02374881   0.00810336 193.06864902  -2.931
## Affluence                    0.00303784   0.00073564 302.35759457   4.129
## Singletons.in.Tract          0.00076577   0.00068685 301.02751482   1.115
## Seniors.in.Tract             0.00036507   0.00086755 304.52067557   0.421
## African.Americans.in.Tract   0.00185376   0.00083859 306.80282282   2.211
## Noncitizens.in.Tract         0.00188332   0.00067656 271.90377206   2.784
## High.BP                     -0.00002254   0.00015180 298.84359504  -0.148
## Binge.Drinking               0.00040978   0.00015953 159.47547855   2.569
## Cancer                      -0.00033713   0.00089039 266.60789774  -0.379
## Asthma                       0.00080796   0.00052879 141.79554831   1.528
## Heart.Disease                0.00319402   0.00114238 211.40118532   2.796
## COPD                        -0.00135854   0.00086479 205.85372606  -1.571
## Smoking                     -0.00019193   0.00019990 251.34232895  -0.960
## Diabetes                    -0.00115859   0.00042840 269.31987743  -2.704
## No.Physical.Activity         0.00031822   0.00017208 238.01560139   1.849
## Obesity                      0.00025223   0.00013934 307.96509372   1.810
## Poor.Sleeping.Habits         0.00024295   0.00013416 297.33747351   1.811
## Poor.Mental.Health          -0.00015970   0.00044866 103.72419434  -0.356
##                             Pr(>|t|)    
## (Intercept)                  0.00379 ** 
## Affluence                  0.0000471 ***
## Singletons.in.Tract          0.26578    
## Seniors.in.Tract             0.67419    
## African.Americans.in.Tract   0.02780 *  
## Noncitizens.in.Tract         0.00575 ** 
## High.BP                      0.88205    
## Binge.Drinking               0.01113 *  
## Cancer                       0.70527    
## Asthma                       0.12876    
## Heart.Disease                0.00565 ** 
## COPD                         0.11773    
## Smoking                      0.33790    
## Diabetes                     0.00728 ** 
## No.Physical.Activity         0.06565 .  
## Obesity                      0.07124 .  
## Poor.Sleeping.Habits         0.07117 .  
## Poor.Mental.Health           0.72260    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation of fixed effects could have been required in summary()
## 
## Correlation of Fixed Effects:
##             (Intr) Afflnc Sng..T Snr..T A.A..T Nnc..T Hgh.BP Bng.Dr Cancer
## Affluence   -0.050                                                        
## Sngltns.n.T -0.055  0.043                                                 
## Snrs.n.Trct  0.395  0.293  0.073                                          
## Afrcn.Am..T  0.241  0.076 -0.405  0.202                                   
## Nnctzns.n.T -0.072  0.153  0.125  0.057 -0.190                            
## High.BP     -0.095  0.157  0.099  0.007 -0.233  0.327                     
## Bing.Drnkng -0.488 -0.040 -0.205 -0.068  0.042 -0.076  0.148              
## Cancer      -0.495 -0.095  0.231 -0.172 -0.074 -0.066 -0.329 -0.019       
## Asthma      -0.269 -0.096 -0.262 -0.121 -0.014  0.211  0.052  0.008 -0.157
## Heart.Dises -0.058  0.077 -0.301 -0.132  0.213 -0.054  0.000  0.034 -0.602
## COPD         0.479  0.009  0.128  0.172 -0.006  0.156  0.058  0.059 -0.212
## Smoking     -0.043  0.105 -0.119 -0.137 -0.105  0.159 -0.082 -0.327  0.157
## Diabetes     0.036 -0.301 -0.078 -0.133 -0.230 -0.253 -0.446  0.075  0.367
## N.Physcl.Ac -0.116  0.034  0.101  0.079  0.059 -0.274  0.004  0.126  0.336
## Obesity     -0.066  0.383  0.398  0.202  0.133  0.193 -0.103 -0.147  0.118
## Pr.Slpng.Hb -0.385 -0.351  0.162 -0.326 -0.321 -0.046 -0.156  0.087  0.028
## Pr.Mntl.Hlt -0.354  0.183 -0.008  0.022  0.051 -0.165  0.028  0.130  0.417
##             Asthma Hrt.Ds COPD   Smokng Diabts N.Ph.A Obesty Pr.S.H
## Affluence                                                          
## Sngltns.n.T                                                        
## Snrs.n.Trct                                                        
## Afrcn.Am..T                                                        
## Nnctzns.n.T                                                        
## High.BP                                                            
## Bing.Drnkng                                                        
## Cancer                                                             
## Asthma                                                             
## Heart.Dises  0.336                                                 
## COPD        -0.322 -0.491                                          
## Smoking      0.144  0.083 -0.475                                   
## Diabetes    -0.106 -0.433 -0.008  0.278                            
## N.Physcl.Ac -0.022 -0.360  0.087 -0.274 -0.169                     
## Obesity     -0.126 -0.021  0.091 -0.220 -0.376 -0.045              
## Pr.Slpng.Hb  0.000  0.239 -0.092 -0.168 -0.060 -0.153 -0.115       
## Pr.Mntl.Hlt -0.437 -0.066 -0.389 -0.028  0.071 -0.086  0.025 -0.081

Testing Rate

testing.data.state <- compiled.stats[[length(daily_filenames)]][, c("Province_State", "Testing_Rate")]
testing.data.state <- testing.data.state[!is.na(testing.data.state$Testing_Rate),]
testing.data.state <- testing.data.state[order(testing.data.state$Testing_Rate),]

col.state <- rep("pink", nrow(testing.data.state))

avg.test.rate <- mean(testing.data.state$Testing_Rate, na.rm = T)

col.state[testing.data.state$Testing_Rate < avg.test.rate] <- "grey"
col.state[testing.data.state$Province_State == "Oklahoma"] <- "lightblue"

par(mar = c(5,6,4,2))
barplot(testing.data.state$Testing_Rate, names.arg = testing.data.state$Province_State, horiz = T, main = "Testing Rate by State", las = 2, cex.axis = 1, cex.names = 0.5, col = col.state, border = F, xlab = "Total number of people tested per 100,000 persons.")
abline(v = avg.test.rate, col = "red")
text(x = avg.test.rate + 10, y = 1, labels = "Average Testing Rate", adj = c(0, 0.5), col = "red")

Pink highlights the last 14 days.

day.first.case <- min(which(US.total$cases.total > 100))
n.days <- nrow(US.total)

twoweek.col <- c(rep("grey", n.days-day.first.case-13), rep("pink", 14))

par(mar = c(5,5,4,2))
barplot(US.total$cases.total[day.first.case:n.days], 
        names = US.total$day[day.first.case:n.days],
        main = "Total COVID-19 cases by Date in US", 
        las = 2, cex.axis = 1, cex.names = 0.5,
        col = twoweek.col, border = F)

barplot(US.total$cases.total[day.first.case:n.days], 
        names = US.total$day[day.first.case:n.days],
        main = "Total COVID-19 cases by Date in US, log scale", 
        las = 2, cex.axis = 1, cex.names = 0.5, log = "y",
        col = twoweek.col, border = F)

barplot(US.total$deaths.total[day.first.case:n.days], 
        names = US.total$day[day.first.case:n.days],
        main = "Total COVID-19 deaths by Date in US", 
        las = 2, cex.axis = 1, cex.names = 0.5,
        col = twoweek.col, border = F)

barplot(US.total$deaths.total[day.first.case:n.days], 
        names = US.total$day[day.first.case:n.days],
        main = "Total COVID-19 deaths by Date in US, log scale", 
        las = 2, cex.axis = 1, cex.names = 0.5, log = "y",
        col = twoweek.col, border = F)

barplot(US.total$rise.cases.total[day.first.case:n.days], 
        names = US.total$day[day.first.case:n.days],
        main = "Rise in Cases of COVID-19 by Date in US", 
        las = 2, cex.axis = 1, cex.names = 0.5,
        col = twoweek.col, border = F)

barplot(US.total$rise.deaths.total[day.first.case:n.days], 
        names = US.total$day[day.first.case:n.days],
        main = "Rise in Deaths of COVID-19 by Date in US", 
        las = 2, cex.axis = 1, cex.names = 0.5,
        col = twoweek.col, border = F)